2021-2022 Capstone Project: Various RF, Analog/Mixed-Signal, and Machine Intelligence Projects

This year’s Capstone project can be conducted solely through on-line means due to on-going COVID-19 related concerns plus the fact that many students are not on campus this Fall. Students should have high speed internet enabling them to remote desktop onto campus computers.

Three specific project areas are available, each appropriate for 1-2 students. All students under my supervision will meet together through zoom calls and there will be an overall team dynamic with individual responsibilities.

Project area #1: RF and Analog IC design

Specific RF and analog IC design projects that may be of interest:

• Wideband tunable bandpass filter based on active inductors. The team will design and implement a high-Q bandpass filter that uses a "simulated" or "virtual" inductor comprised of a gyrator. This type of circuit is useful for software defined radio applications.
• Low-power SAR ADC using non-binary weighted capacitor arrays. This is a type of ADC that can achieve high resolution at low power. This project involves mixed-signal circuit design and would be good for students interested in a career designing ICs for biomedical applications.
• Other RF and analog IC design project suggestions will certainly be considered if I think I could supervise the project to a successful completion.

Project area #2: Digital IC design

• Machine Intelligence accelerator. This project will create an IC to accelerate the training of neural networks. Specifically, the implementation of CRAM (Computational RAM) will be a focus point for the project. This type of memory can operate on its own contents, without needing to send data over a bus where it is processed elsewhere and then returned to the memory.
• FPGA-based Cryptocurrency miner. This project will develop Verilog code for mining bitcoin, with the goal of deploying the code to an FGPA and mining coins.
• Other Digital IC design project suggestions will certainly be considered if I think I could supervise the project to a successful completion.

Project area #3: Machine Intelligence for Silicon Photonics

Students in this sub-project will be co-supervised by Dr. Ye.

The goal of this project is to use artificial neural networks (ANNs) to accelerate the design of silicon photonics components. A silicon photonics structure will be parameterized and multiple versions of the structure will be simulated using a commericial finite difference time domain (FDTD) simulator (Lumerical). The performance of the parameterized structure will be evaluated so that for each set of input parameters there is a corresponding set of simulated output parameters. An artificial neural network will be trained using machine learning techniques and the simulated data. The resulting ANN will be used to accelerate the design flow of similar parameterized silicon photonics structures by avoiding many of the FDTD simulations. Part of the project will be to determine the energy savings and increased computational efficiency and speed of the ANN-based design flow compared to the conventional FDTD-based design methodology.

This project is for students who are interested in machine learning applications, photonics and silicon photonics, and neural network applications. Students should have some knowledge of Python (the project will use PyTorch). Knowledge of finite element simulations and Lumerical would be an advantage.